| 运动目标状态序贯贝叶斯滤波 |
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| 引用本文： | 蒋富勤,赵航芳.运动目标状态序贯贝叶斯滤波[J].杭州电子科技大学学报,2011(4):35-37. |
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| 作者姓名： | 蒋富勤 赵航芳 |
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| 作者单位： | 杭州应用声学研究所; |
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| 基金项目： | 国家安全重大基础基金资助项目(613110020102) |
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| 摘 要： | 状态-空间模型是一个通用和宽容的模型,它包含了描述系统所要求的所有相关信息.贝叶斯滤波方法是解决状态估计问题的一般框架.当有新数据可用时,序贯滤波方法可以实现估计和更新待估参量.对于运动目标状态跟踪问题,利用状态-空间模型进行建模,并采用序贯贝叶斯方法进行处理,如扩展卡尔曼滤波、unscented卡尔曼滤波和质点滤波....
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| 关 键 词： | 序贯贝叶斯滤波 目标运动分析 状态-空间模型 |
Sequential Bayesian Filtering for the States of a Moving Target |
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| JIANG Fu-qin,ZHAO Hang-fang.Sequential Bayesian Filtering for the States of a Moving Target[J].Journal of Hangzhou Dianzi University,2011(4):35-37. |
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| Authors: | JIANG Fu-qin ZHAO Hang-fang |
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| Affiliation: | JIANG Fu-qin,ZHAO Hang-fang(Hangzhou Acoustics Research Institute,Hangzhou Zhejiang 310012,China) |
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| Abstract: | State-space model is a versatile and robust model.It includes all relevant information required to describe the system under investigation.Bayesian filtering provides a general framework for dynamitic estimation problems.Sequential filtering is used to estimate and update the unknown parameters of a system as data become available.State-space model and sequential Bayesian filtering are used for tracking the states of a moving target.This paper gives the result of three Bayesian filtering methods,namely exte... |
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| Keywords: | sequential Bayesian filtering target motion analysis state-space model |
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